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Social Media Domain Implementation Status

Project Overview

Feature: Complete socialmedia domain implementation from empty stubs to production
Total Estimated Time: 32 hours across 3 phases
Approach: Parallel development with multiple AI agents
Target: >85% test coverage, PostgreSQL migration, PRAW Reddit integration, OpenRouter LLM sentiment analysis

Progress Summary

Phase Status Completed Total Progress Est. Time
Phase 1: Foundation 🟡 Not Started 0 4 0% 12h
Phase 2: API Integration 🟡 Not Started 0 4 0% 12h
Phase 3: Integration 🟡 Not Started 0 3 0% 8h
Overall Progress 🟡 Not Started 0 11 0% 32h

Phase 1: Foundation (12 hours)

🏗️ Database & Core Models

Task Agent Status Progress Time Priority
1.1 Database Schema Migration Database Specialist 🟡 Not Started 0% 3h 🔴 Blocking
1.2 SQLAlchemy Entity Implementation Entity Specialist 🟡 Not Started 0% 3h 🔴 Blocking
1.3 Domain Model Enhancement Domain Specialist 🟡 Not Started 0% 3h 🔴 Blocking
1.4 Repository Implementation Repository Specialist 🟡 Not Started 0% 3h 🟠 Medium

Phase 1 Dependencies

  • Task 1.1 → Task 1.2 (Entity requires database schema)
  • Task 1.4 depends on Tasks 1.1 + 1.2
  • Task 1.3 can run parallel with others

Phase 1 Acceptance Criteria

  • PostgreSQL table social_media_posts with TimescaleDB + pgvectorscale
  • SocialMediaPostEntity with proper field mappings and transformations
  • SocialPost domain model with validation and business rules
  • SocialRepository with vector similarity search and sentiment aggregation

Phase 2: API Integration & Processing (12 hours)

🔌 Clients & Services

Task Agent Status Progress Time Priority
2.1 Reddit Client Implementation API Integration Specialist 🟡 Not Started 0% 4h 🔴 Blocking
2.2 OpenRouter Sentiment Analysis LLM Integration Specialist 🟡 Not Started 0% 3h 🟠 Medium
2.3 Vector Embedding Generation ML Integration Specialist 🟡 Not Started 0% 2h 🟠 Medium
2.4 Service Layer Implementation Service Integration Specialist 🟡 Not Started 0% 3h 🟠 Medium

Phase 2 Dependencies

  • All tasks can run in parallel initially
  • Task 2.4 depends on completion of Tasks 2.1, 2.2, 2.3

Phase 2 Acceptance Criteria

  • PRAW Reddit client with rate limiting and error handling
  • OpenRouter sentiment analysis with social media-specific prompts
  • Vector embeddings (1536-dim) for titles and content using text-embedding-3-large
  • SocialMediaService orchestrating collection, sentiment, and embeddings

Phase 3: Integration & Validation (8 hours)

🎯 AgentToolkit & Pipeline

Task Agent Status Progress Time Priority
3.1 AgentToolkit Integration Agent Integration Specialist 🟡 Not Started 0% 3h 🔴 High
3.2 Dagster Pipeline Implementation Pipeline Specialist 🟡 Not Started 0% 2h 🟠 Medium
3.3 Comprehensive Testing Suite Testing Specialist 🟡 Not Started 0% 3h 🔴 High

Phase 3 Dependencies

  • Task 3.1 depends on Task 2.4 (SocialMediaService)
  • Task 3.2 depends on Task 2.4
  • Task 3.3 can start after any component is implemented

Phase 3 Acceptance Criteria

  • AgentToolkit RAG methods: get_reddit_sentiment(), get_reddit_stock_info(), etc.
  • Daily Dagster pipeline with sentiment analysis and embedding generation
  • >85% test coverage with VCR cassettes and mocked dependencies

Current Blocking Issues

Issue Impact Affected Tasks Resolution
No active blocking issues - - Ready to start Phase 1

Implementation Readiness

Prerequisites Status

Requirement Status Notes
PostgreSQL + Extensions ✅ Available TimescaleDB + pgvectorscale ready
Reddit API Credentials ⚠️ Required Need REDDIT_CLIENT_ID, REDDIT_CLIENT_SECRET
OpenRouter API Access ✅ Available Existing OpenRouterClient integration
Database Migration System ✅ Available Existing migration infrastructure
Testing Framework ✅ Available pytest, pytest-vcr, pytest-asyncio

Risk Assessment

Risk Level Tasks Mitigation
🔴 High 2.1 (Reddit Client) Use proven PRAW library, implement circuit breaker
🟠 Medium 1.1, 1.4, 2.2, 2.4 Follow existing news domain patterns
🟢 Low 1.2, 1.3, 2.3, 3.1, 3.2, 3.3 Standard implementation patterns

Key Success Metrics

Technical Metrics

  • Database Performance: <1s vector similarity queries for top 10 results
  • API Performance: <2s social context generation for AI agents
  • Processing Performance: <5s batch processing for 1000 posts
  • Test Coverage: >85% across all socialmedia domain components
  • Data Quality: >80% posts with reliable sentiment analysis

Integration Metrics

  • AgentToolkit Integration: 4 RAG methods implemented and tested
  • Dagster Pipeline: Daily automated collection with monitoring
  • Architecture Consistency: Follows news domain patterns exactly
  • Error Resilience: Graceful degradation on API failures

Business Metrics

  • Data Collection: 400+ posts collected daily from financial subreddits
  • Sentiment Analysis: Structured scoring with confidence levels
  • Semantic Search: Vector-based similarity search operational
  • Agent Context: Rich social media context for trading decisions

Next Steps

Immediate Actions (Next Sprint)

  1. 🚀 Start Phase 1: Begin database schema migration (Task 1.1)
  2. 📋 Environment Setup: Configure Reddit API credentials
  3. 👥 Agent Assignment: Assign specialized agents to parallel tasks
  4. 📊 Progress Tracking: Update status after each task completion

Phase Transition Criteria

Phase 1 → Phase 2: All foundation tasks complete, database operational
Phase 2 → Phase 3: Service layer operational, sentiment and embeddings working
Phase 3 → Production: All tests passing, AgentToolkit integration complete

Change Log

Date Change Impact Updated By
2024-08-30 Initial status tracking setup Baseline established System

Notes and Observations

Implementation Strategy:

  • Leverage existing news domain as reference implementation
  • Prioritize blocking tasks (database, core models) first
  • Enable parallel development in Phase 2 for efficiency
  • Comprehensive testing throughout to maintain >85% coverage

Key Dependencies:

  • Reddit API reliability and rate limiting compliance
  • OpenRouter LLM performance for sentiment analysis
  • PostgreSQL vector extension performance at scale
  • Integration with existing TradingAgents configuration

Success Indicators:

  • Clean migration from file-based to PostgreSQL storage
  • Reliable daily data collection without manual intervention
  • AI agents receiving rich social context within performance targets
  • Production-ready error handling and monitoring